DEVELOPMENT OF ANALYTICAL TOOLS FOR SOFTWARE FUNCTIONAL STATE ESTIMATIONTHROUGHOUT THE LIFECYCLE

Abstract

This paper presents a comprehensive approach to developing analytical tools for assessing software functional states (SFS) throughout the software development life cycle (SDLC). In contrast to traditional, isolated quality control, the concept presented here considers a software system as a dynamic object that exists in a series of formalized states and their transitions. The functional state, as an integrated model, encompasses data from different phases, including requirements gathering, architectural design, implementation, testing, deployment, operation, and maintenance.  Analytics tools provide six types of analytics: descriptive, diagnostic, predictive, prescriptive, comparative, and contextually adaptive. Each type of analytics defines a corresponding set of tools – models, methods, algorithms and metrics – for detecting deviations, analyzing cause-and-effect relationships, predicting degradation, choosing response strategies and adapting solutions to the operating environment. In addition, an integrated system of SFS metrics is proposed for assessing the stability, reliability, security, adaptability and risk of software. The architecture of the analytical tools is presented based on the C4 framework, describing in detail the context level, container level, component level and code level. Also, the structural and behavioral diagrams are provided, data formats, visualization tools, and user interface examples. In addition, the article explores scenarios for the application of SFS analytics in test mode. Taken together, the proposed tools allow for a controlled, adaptive, and self-regulating path for software development, which is a critical point for complex, high-risk, and dynamic software environments. The results can be used to develop decision support systems for software development, testing, deployment, and maintenance.